Most of the proposed models are discriminative, among the few works carried out for generative models for SF and IC, [1]}, [2]} have shown that a generative model is relatively better than a discriminative model in a situation where data is scarce. One possible direction for generative models is to apply data augmentation to automatically create additional training data [3]}, [4]}, [5]}, [6]}, [7]}, [8]}. The main challenge for data augmentation is to generate diverse and fluent synthetic utterance, which preserve the semantics of the original utterance.
